Unlocking the Power of Open LLMs and GenerativeAI: A 10-Step Guide to Finding Your Perfect Language Model

As a leading healthcare company, HealthInc recognized the potential of leveraging LLMs (large language models) for their customer support chatbots. However, due to the heavily regulated nature of the industry, concerns arose regarding the use of closed-source LLMs like ChatGPT and Bard. After careful consideration, HealthInc made the decision to fine-tune and utilize one of the top-performing open-source LLMs available. Latest techniques such as LoRA (Low-Rank Adaptation) made it possible to fine-tune LLM's quickly with far smaller training data requirements.

To identify the most suitable LLM, the HealthInc team visited "the Huggingface Open LLM Leaderboard", which provides LLM rankings based on multiple benchmark scores. Given the significance of the decision, the HealthInc team prioritized a thorough understanding of these scores to ensure the selected model aligns best with their use case.

You may also be in the same situation soon, or you are already in! Let us delve into the details of metrics being used to rank LLMs by the Huggingface Open LLM Leaderboard.

What is Open LLM?

An open large language model (LLM) is a language model that is openly accessible and utilized by developers and researchers. These models are trained on large datasets of text and code, which allows them to learn the statistical relationships between words and phrases. Open LLMs can be used for a variety of tasks, including question answering, natural language inference, text summarization, code generation, and creative text formats.

What is so 'Open' about them?

The openness of these models generally implies the availability of their underlying architecture, parameters, and in some cases, even the training data. Open LLMs are designed to be user-friendly and accessible, allowing users to harness their power without extensive expertise in machine learning or computational linguistics.

Where do we find them?

Open LLMs are typically made available through platforms like Hugging Face or TensorFlow, allowing developers to fine-tune the models for specific tasks or use them as-is for a wide range of language-related applications.

How Huggingface ranks them?

Rankings are based on the average scores across four different metrics:

These metrics provide a comprehensive evaluation of different aspects of language model performance, including reasoning, common-sense understanding, multilingual and multimodal capabilities, and the ability to provide accurate and truthful answers.

Here is a comparison of these four metrics:

How to select the best Open LLM for your requirement?

I suggest you adopt 10 step approach in finding the best fit Open LLM for your use case:

1. Identify the specific use case or task for which an LLM is required.
2. Understand the significance of each metric and how it aligns with the use case.
3. Prioritize metrics that are most relevant to the specific use case and desired LLM capabilities.
4. Review the rankings and performance of LLMs on the leaderboard based on the selected metrics.
5. Consider additional factors such as model size, computational requirements, required data, and other available fine-tuning options.
6. Evaluate the documentation and support available for each LLM, including the availability of pre-trained models and example code.
7. Explore community feedback and reviews on the LLMs of interest.
8. Read the licensing terms carefully.
9. Based on the analysis and considerations, select the most suitable open LLM from the Huggingface Leaderboard for integration into the desired application or use case.
10. Keep in mind: the best model for your use case may not be the number 1 ranked model on the leaderboard!

The leaderboard is a good starting point, but you will need to consider your specific use case and requirements in order to select the best model for your needs.

Let me know your thoughts!

Credits:

• https://github.com/EleutherAI/lm-evaluation-harness

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